mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
116 lines
3.7 KiB
C++
116 lines
3.7 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/pybind/main.h"
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namespace fastdeploy {
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void BindRuntime(pybind11::module&);
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void BindFDModel(pybind11::module&);
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void BindVision(pybind11::module&);
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pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype) {
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pybind11::dtype dt;
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if (fd_dtype == FDDataType::INT32) {
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dt = pybind11::dtype::of<int32_t>();
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} else if (fd_dtype == FDDataType::INT64) {
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dt = pybind11::dtype::of<int64_t>();
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} else if (fd_dtype == FDDataType::FP32) {
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dt = pybind11::dtype::of<float>();
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} else if (fd_dtype == FDDataType::FP64) {
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dt = pybind11::dtype::of<double>();
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} else {
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FDASSERT(false, "The function doesn't support data type of " +
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FDDataTypeStr(fd_dtype) + ".");
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}
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return dt;
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}
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FDDataType NumpyDataTypeToFDDataType(const pybind11::dtype& np_dtype) {
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if (np_dtype.is(pybind11::dtype::of<int32_t>())) {
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return FDDataType::INT32;
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} else if (np_dtype.is(pybind11::dtype::of<int64_t>())) {
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return FDDataType::INT64;
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} else if (np_dtype.is(pybind11::dtype::of<float>())) {
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return FDDataType::FP32;
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} else if (np_dtype.is(pybind11::dtype::of<double>())) {
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return FDDataType::FP64;
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}
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FDASSERT(false, "NumpyDataTypeToFDDataType() only support "
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"int32/int64/float32/float64 now.");
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return FDDataType::FP32;
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}
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void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
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bool share_buffer) {
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tensor->dtype = NumpyDataTypeToFDDataType(pyarray.dtype());
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tensor->shape.insert(tensor->shape.begin(), pyarray.shape(),
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pyarray.shape() + pyarray.ndim());
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if (share_buffer) {
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tensor->external_data_ptr = pyarray.mutable_data();
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} else {
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tensor->data.resize(pyarray.nbytes());
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memcpy(tensor->data.data(), pyarray.mutable_data(), pyarray.nbytes());
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}
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}
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#ifdef ENABLE_VISION
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int NumpyDataTypeToOpenCvType(const pybind11::dtype& np_dtype) {
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if (np_dtype.is(pybind11::dtype::of<int32_t>())) {
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return CV_32S;
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} else if (np_dtype.is(pybind11::dtype::of<int8_t>())) {
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return CV_8U;
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} else if (np_dtype.is(pybind11::dtype::of<uint8_t>())) {
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return CV_8U;
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} else if (np_dtype.is(pybind11::dtype::of<float>())) {
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return CV_32F;
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} else {
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FDASSERT(
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false,
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"NumpyDataTypeToOpenCvType() only support int32/int8/uint8/float32 "
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"now.");
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}
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return CV_8U;
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}
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cv::Mat PyArrayToCvMat(pybind11::array& pyarray) {
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auto cv_type = NumpyDataTypeToOpenCvType(pyarray.dtype());
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FDASSERT(
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pyarray.ndim() == 3,
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"Require rank of array to be 3 with HWC format while converting it to "
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"cv::Mat.");
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int channel = *(pyarray.shape() + 2);
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int height = *(pyarray.shape());
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int width = *(pyarray.shape() + 1);
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return cv::Mat(height, width, CV_MAKETYPE(cv_type, channel),
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pyarray.mutable_data());
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}
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#endif
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PYBIND11_MODULE(fastdeploy_main, m) {
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m.doc() =
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"Make programer easier to deploy deeplearning model, save time to save "
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"the world!";
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BindRuntime(m);
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BindFDModel(m);
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#ifdef ENABLE_VISION
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auto vision_module =
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m.def_submodule("vision", "Vision module of FastDeploy.");
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BindVision(vision_module);
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#endif
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}
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} // namespace fastdeploy
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